temp = os.dup(sys.stdout.fileno())
try:
url = '/%s/comp.html' % m_dir_id
if not os.path.exists(os.path.join('..', m_dir_id, 'comp.html')):
chebi = parse_simple(get_chebi())
reader = libsbml.SBMLReader()
input_document = reader.readSBML(groups_sbml)
input_model = input_document.getModel()
# sbml -> tulip graph
logging.info('sbml -> tlp')
graph, c_id2info, c_id2outs, chebi, ub_sps = import_sbml(input_model, groups_sbml)
try:
n2xy = parse_layout_sbml(groups_sbml)
except LoPlError:
n2xy = None
fc, (n2lo, e2lo), hidden_c_ids, c_id_hidden_ubs = graph2geojson(c_id2info, c_id2outs, graph, n2xy, onto=chebi)
c_id2out_c_id = {}
for c_id, info in c_id2info.items():
if c_id not in fc:
continue
_, _, (_, out_c_id) = info
if out_c_id and out_c_id in fc:
c_id2out_c_id[c_id] = out_c_id
if not n2xy or gen_sbml:
groups_document = reader.readSBML(groups_sbml)
groups_model = groups_document.getModel()
try:
url = '/%s/comp.html' % m_dir_id
if not os.path.exists(os.path.join('..', m_dir_id, 'comp.html')):
chebi = parse_simple(get_chebi())
reader = libsbml.SBMLReader()
input_document = reader.readSBML(groups_sbml)
input_model = input_document.getModel()
# sbml -> tulip graph
logging.info('sbml -> tlp')
graph, c_id2info, c_id2outs, chebi, ub_sps = import_sbml(
input_model, groups_sbml)
try:
n2xy = parse_layout_sbml(groups_sbml)
except LoPlError:
n2xy = None
fc, (n2lo,
e2lo), hidden_c_ids, c_id_hidden_ubs = graph2geojson(c_id2info,
c_id2outs,
graph,
n2xy,
onto=chebi)
c_id2out_c_id = {}
for c_id, info in c_id2info.items():
if c_id not in fc:
continue
_, _, (_, out_c_id) = info
if out_c_id and out_c_id in fc:
def process_sbml(sbml,
verbose,
ub_ch_ids=None,
web_page_prefix=None,
generalize=True,
log_file=None,
id2mask=None,
layer2mask=DEFAULT_LAYER2MASK,
tab2html=None,
title=None,
h1=None,
id2color=None,
tabs={ABOUT_TAB, DOWNLOAD_TAB},
info='',
invisible_layers=None,
sbgn=True,
cytoscape=True):
"""
Generalizes and visualizes a given SBML model.
:param sbml: a path to the input SBML file
:param verbose: if logging information should be printed
:param ub_ch_ids: optional, ChEBI ids to be considered as ubiquitous. If left None, will be calculated automatically.
:param web_page_prefix: optional, how this model's webpage will be identified.
If left None an identifier will be generated based on the SBML file's md5.
:param generalize: optional, whether the generalization should be performed. The default is True
:param log_file: optional, a file where the logging information should be redirected
(only needed if verbose is set to True)
:param id2mask: optional,
:param layer2mask: optional, a dict storing the correspondence between a layer name and an its id mask
:param tab2html: optional,
:param title: optional, the title for the web page
:param h1: optional, the main header of the web page
:param id2color: optional,
:param tabs: optional, a set of names of tabs that should be shown
:param info: optional, additional information to be displayed in the bottom of the web page
:param invisible_layers: optional, the layers of the visualized metabolic map that should be hidden
:return: void
"""
# Read the SBML
reader = libsbml.SBMLReader()
doc = reader.readSBML(sbml)
model = doc.getModel()
if not model:
raise Exception(
"The model should be in SBML format, check your file %s" % sbml)
model_id = model.getId()
if not model_id:
sbml_name = os.path.splitext(os.path.basename(sbml))[0]
model.setId(sbml_name)
model_id = sbml_name
# Prepare the output directories
web_page_prefix = web_page_prefix if web_page_prefix else check_md5(sbml)
sbml_dir = dirname(abspath(sbml))
directory = os.path.join(sbml_dir, web_page_prefix)
if not os.path.exists(directory):
os.makedirs(directory)
lib_path = os.path.join(directory, 'lib')
if not os.path.exists(lib_path):
copytree(get_lib(), lib_path)
# Prepare the logger
if verbose:
logging.captureWarnings(True)
logging.basicConfig(level=logging.INFO,
format='%(asctime)s: %(message)s',
datefmt="%Y-%m-%d %H:%M:%S",
filename=log_file)
# Generalize the model if needed
groups_sbml = os.path.join(directory, '%s_with_groups.xml' % model_id)
gen_sbml = os.path.join(directory, '%s_generalized.xml' % model_id)
if check_for_groups(sbml, SBO_CHEMICAL_MACROMOLECULE,
GROUP_TYPE_UBIQUITOUS):
if sbml != groups_sbml:
if not libsbml.SBMLWriter().writeSBMLToFile(doc, groups_sbml):
raise Exception("Could not write your model to %s" %
groups_sbml)
else:
chebi = parse_simple(get_chebi())
if generalize:
logging.info('Generalizing the model...')
generalize_model(sbml,
chebi,
groups_sbml,
gen_sbml,
ub_chebi_ids=ub_ch_ids)
else:
gen_sbml = None
logging.info('Ubiquitizing the model...')
ubiquitize_model(sbml, chebi, groups_sbml, ub_chebi_ids=ub_ch_ids)
# Visualize the model
reader = libsbml.SBMLReader()
input_document = reader.readSBML(groups_sbml)
input_model = input_document.getModel()
root, c_id2info, c_id2outs, chebi, ub_sps = import_sbml(
input_model, groups_sbml)
c_id2out_c_id = {}
for c_id, c_info in c_id2info.items():
_, _, (_, out_c_id) = c_info
if out_c_id:
c_id2out_c_id[c_id] = out_c_id
try:
n2xy = parse_layout_sbml(sbml)
if n2xy:
logging.info('Found layout in the model...')
r_size = next((n2xy[r.getId()][1][0]
for r in input_model.getListOfReactions()
if r.getId() in n2xy), None)
if r_size:
scale_factor = REACTION_SIZE / r_size
if scale_factor != 1:
keys = n2xy.keys()
for n_id in keys:
value = n2xy[n_id]
if isinstance(value, dict):
value = {
r_id:
(scale(xy,
scale_factor), scale(wh, scale_factor))
for (r_id, (xy, wh)) in value.items()
}
else:
xy, wh = value
value = scale(xy, scale_factor), scale(
wh, scale_factor)
n2xy[n_id] = value
except LoPlError:
n2xy = None
fc, (n2lo, e2lo), hidden_c_ids, c_id_hidden_ubs = \
graph2geojson(c_id2info, c_id2outs, root, n2xy, id2mask=id2mask, onto=chebi,
colorer=color if not id2color else lambda graph: color_by_id(graph, id2color))
if n2lo:
groups_document = reader.readSBML(groups_sbml)
groups_model = groups_document.getModel()
if gen_sbml:
gen_document = reader.readSBML(gen_sbml)
gen_model = gen_document.getModel()
else:
gen_model = False
save_as_layout_sbml(groups_model, gen_model, groups_sbml, gen_sbml,
n2lo, ub_sps)
if sbgn:
groups_sbgn = os.path.join(directory, '%s.sbgn' % model_id)
gen_sbgn = os.path.join(directory,
'%s_generalized.sbgn' % model_id)
try:
save_as_sbgn(n2lo, e2lo, groups_model, groups_sbgn)
logging.info(' exported as SBGN %s' % groups_sbgn)
except Exception as e:
logging.error("Didn't manage to save to SBGN: %s" % e)
if gen_model:
try:
save_as_sbgn(n2lo, e2lo, gen_model, gen_sbgn)
logging.info(' exported as SBGN %s' % groups_sbgn)
except Exception as e:
logging.error("Didn't manage to save to SBGN: %s" % e)
if cytoscape:
out_json = os.path.join(directory, '%s.cyjs' % model_id)
save_as_cytoscape_json(n2lo, model, out_json, ub_sps)
logging.info(' exported as Cytoscape json %s' % out_json)
if gen_model:
out_json = os.path.join(directory,
'%s_generalized.cyjs' % model_id)
save_as_cytoscape_json(n2lo, gen_model, out_json, ub_sps)
# Serialize the result
serialize(directory=directory,
m_dir_id=web_page_prefix,
input_model=input_model,
c_id2level2features=fc,
c_id2out_c_id=c_id2out_c_id,
hidden_c_ids=hidden_c_ids,
c_id_hidden_ubs=c_id_hidden_ubs,
tabs=tabs,
groups_sbml=groups_sbml,
layer2mask=layer2mask,
tab2html=tab2html,
title=title,
h1=h1,
invisible_layers=invisible_layers)
